GitHub / SciML / DiffEqFlux.jl

Pre-built implicit layer architectures with O(1) backprop, GPUs, and stiff+non-stiff DE solvers, demonstrating scientific machine learning (SciML) and physics-informed machine learning methods

JSON API: https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/SciML%2FDiffEqFlux.jl

Commit Stats

Commits: 1474
Authors: 93
Mean commits per author: 15.85
Development Distribution Score: 0.626
More commit stats: https://commits.ecosyste.ms/hosts/GitHub/repositories/SciML/DiffEqFlux.jl

Topics: delay-differential-equations, differential-equations, differentialequations, neural-dde, neural-differential-equations, neural-jump-diffusions, neural-networks, neural-ode, neural-pde, neural-sde, neural-sdes, ordinary-differential-equations, partial-differential-equations, physics-informed-learning, pinn, scientific-ai, scientific-machine-learning, scientific-ml, stiff-ode, stochastic-differential-equations

Funding links: https://github.com/sponsors/SciML

Files

Dependencies